The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Medical Resonance Imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Recognizing anatomical structures within digitized medical images presents multiple challenges. For example, a first concern relates to the accuracy of recognition of anatomical structures within an image. A second area of concern is the speed of recognition. Because medical images are an aid for a doctor to diagnose a disease or condition, the speed with which an image can be processed and structures within that image recognized can be of the utmost importance to the doctor reaching an early diagnosis. Hence, there is a need for improving recognition techniques that provide accurate and fast recognition of anatomical structures and possible abnormalities in medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”). Such 2-D or 3-D images are processed using medical image recognition techniques to determine the presence of anatomical structures such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan; it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
One general method of automatic image processing employs feature based recognition techniques to determine the presence of anatomical structures in medical images. However, feature based recognition techniques can suffer from accuracy problems.
Automatic image processing and recognition of structures within a medical image is generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images and identify anatomical structures including possible abnormalities for further review. Such possible abnormalities are often called candidates and are considered to be generated by the CAD system based upon the medical images.
During the reading and analysis of medical images, whether performed manually by a doctor or performed automatically by automatic image processing systems, a region of interest may be identified in a first or primary medical image. Due to the wide variety of different imaging modalities as well as the infinite variety of individual patient anatomies, it is no trivial task to locate the corresponding region of interest in one or more other medical images, where the corresponding region of interest corresponds to the identified region in the first or primary image. This is especially difficult when the medical images are projective images such as X-ray images in which from various directions and angles, imaged anatomy may be obstructed or obscured by other anatomical structures and it can be near impossible to obtain a direct point-to-point correspondence location from one image to the next.
Therefore there is a need for improved systems and methods to facilitate detection and visualization of a region of interest in one or more medical images based upon a corresponding region of interest in a primary medical image.